




"""simple docstring"""







import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
		from transformers.models.esm.modeling_esm import (
		    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    EsmEmbeddings,
		    create_position_ids_from_input_ids,
		)






class    SCREAMING_SNAKE_CASE__       :


						def __init__(				self					:      Union[str, Any]    ,    lowerCAmelCase					:      str    ,    lowerCAmelCase					:      Tuple=13    ,    lowerCAmelCase					:      Union[str, Any]=7    ,    lowerCAmelCase					:      str=False    ,    lowerCAmelCase					:      Optional[int]=True    ,    lowerCAmelCase					:      str=False    ,    lowerCAmelCase					:      List[str]=True    ,    lowerCAmelCase					:      Union[str, Any]=33    ,    lowerCAmelCase					:      List[Any]=32    ,    lowerCAmelCase					:      List[Any]=5    ,    lowerCAmelCase					:      Tuple=4    ,    lowerCAmelCase					:      Tuple=37    ,    lowerCAmelCase					:      Tuple="gelu"    ,    lowerCAmelCase					:      int=0.1    ,    lowerCAmelCase					:      int=0.1    ,    lowerCAmelCase					:      Dict=512    ,    lowerCAmelCase					:      Tuple=16    ,    lowerCAmelCase					:      Tuple=2    ,    lowerCAmelCase					:      str=0.02    ,    lowerCAmelCase					:      Optional[int]=3    ,    lowerCAmelCase					:      str=4    ,    lowerCAmelCase					:      List[Any]=None    ,    ):
										lowerCAmelCase						=					parent
										lowerCAmelCase						=					batch_size
										lowerCAmelCase						=					seq_length
										lowerCAmelCase						=					is_training
										lowerCAmelCase						=					use_input_mask
										lowerCAmelCase						=					use_token_type_ids
										lowerCAmelCase						=					use_labels
										lowerCAmelCase						=					vocab_size
										lowerCAmelCase						=					hidden_size
										lowerCAmelCase						=					num_hidden_layers
										lowerCAmelCase						=					num_attention_heads
										lowerCAmelCase						=					intermediate_size
										lowerCAmelCase						=					hidden_act
										lowerCAmelCase						=					hidden_dropout_prob
										lowerCAmelCase						=					attention_probs_dropout_prob
										lowerCAmelCase						=					max_position_embeddings
										lowerCAmelCase						=					type_vocab_size
										lowerCAmelCase						=					type_sequence_label_size
										lowerCAmelCase						=					initializer_range
										lowerCAmelCase						=					num_labels
										lowerCAmelCase						=					num_choices
										lowerCAmelCase						=					scope


						def 	__lowercase			(				self					:      Dict       ):
										lowerCAmelCase						=					ids_tensor([self.batch_size, self.seq_length]    ,    self.vocab_size       )

										lowerCAmelCase						=					None
										if self.use_input_mask:
														lowerCAmelCase						=					random_attention_mask([self.batch_size, self.seq_length]       )

										lowerCAmelCase						=					None
										lowerCAmelCase						=					None
										lowerCAmelCase						=					None
										if self.use_labels:
														lowerCAmelCase						=					ids_tensor([self.batch_size]    ,    self.type_sequence_label_size       )
														lowerCAmelCase						=					ids_tensor([self.batch_size, self.seq_length]    ,    self.num_labels       )
														lowerCAmelCase						=					ids_tensor([self.batch_size]    ,    self.num_choices       )

										lowerCAmelCase						=					self.get_config()

										return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


						def 	__lowercase			(				self					:      List[str]       ):
										return EsmConfig(
										    vocab_size=self.vocab_size    ,    hidden_size=self.hidden_size    ,    pad_token_id=1    ,    num_hidden_layers=self.num_hidden_layers    ,    num_attention_heads=self.num_attention_heads    ,    intermediate_size=self.intermediate_size    ,    hidden_act=self.hidden_act    ,    hidden_dropout_prob=self.hidden_dropout_prob    ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,    max_position_embeddings=self.max_position_embeddings    ,    type_vocab_size=self.type_vocab_size    ,    initializer_range=self.initializer_range    ,    )


						def 	__lowercase			(				self					:      int    ,    lowerCAmelCase					:      Tuple    ,    lowerCAmelCase					:      List[Any]    ,    lowerCAmelCase					:      str    ,    lowerCAmelCase					:      str    ,    lowerCAmelCase					:      List[Any]    ,    lowerCAmelCase					:      str       ):
										lowerCAmelCase						=					EsmModel(config=__snake_case       )
										model.to(__snake_case       )
										model.eval()
										lowerCAmelCase						=					model(__snake_case    ,    attention_mask=__snake_case       )
										lowerCAmelCase						=					model(__snake_case       )
										lowerCAmelCase						=					model(__snake_case       )

										self.parent.assertEqual(result.last_hidden_state.shape    ,    (self.batch_size, self.seq_length, self.hidden_size)       )
										self.parent.assertEqual(result.pooler_output.shape    ,    (self.batch_size, self.hidden_size)       )


						def 	__lowercase			(				self					:      Dict    ,    lowerCAmelCase					:      int    ,    lowerCAmelCase					:      Dict    ,    lowerCAmelCase					:      Dict    ,    lowerCAmelCase					:      Any    ,    lowerCAmelCase					:      List[Any]    ,    lowerCAmelCase					:      Union[str, Any]       ):
										lowerCAmelCase						=					EsmForMaskedLM(config=__snake_case       )
										model.to(__snake_case       )
										model.eval()
										lowerCAmelCase						=					model(__snake_case    ,    attention_mask=__snake_case    ,    labels=__snake_case       )
										self.parent.assertEqual(result.logits.shape    ,    (self.batch_size, self.seq_length, self.vocab_size)       )


						def 	__lowercase			(				self					:      Union[str, Any]    ,    lowerCAmelCase					:      Union[str, Any]    ,    lowerCAmelCase					:      str    ,    lowerCAmelCase					:      str    ,    lowerCAmelCase					:      int    ,    lowerCAmelCase					:      Union[str, Any]    ,    lowerCAmelCase					:      int       ):
										lowerCAmelCase						=					self.num_labels
										lowerCAmelCase						=					EsmForTokenClassification(config=__snake_case       )
										model.to(__snake_case       )
										model.eval()
										lowerCAmelCase						=					model(__snake_case    ,    attention_mask=__snake_case    ,    labels=__snake_case       )
										self.parent.assertEqual(result.logits.shape    ,    (self.batch_size, self.seq_length, self.num_labels)       )




						def 	__lowercase			(				self					:      List[Any]       ):
										lowerCAmelCase						=					self.prepare_config_and_inputs()
										(
										    lowerCAmelCase      
										)						=					config_and_inputs
										lowerCAmelCase						=					{'input_ids': input_ids, 'attention_mask': input_mask}
										return config, inputs_dict






@require_torch
class    SCREAMING_SNAKE_CASE__       (       lowerCamelCase__    ,					lowerCamelCase__    ,					unittest.TestCase       ):
						_a         =    False

						_a         =    (
						    (
						        EsmForMaskedLM,
						        EsmModel,
						        EsmForSequenceClassification,
						        EsmForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						_a         =    ()
						_a         =    (
						    {
						        'feature-extraction': EsmModel,
						        'fill-mask': EsmForMaskedLM,
						        'text-classification': EsmForSequenceClassification,
						        'token-classification': EsmForTokenClassification,
						        'zero-shot': EsmForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_a         =    True


						def 	__lowercase			(				self					:      str       ):
										lowerCAmelCase						=					EsmModelTester(self       )
										lowerCAmelCase						=					ConfigTester(self    ,    config_class=__snake_case    ,    hidden_size=37       )


						def 	__lowercase			(				self					:      Tuple       ):
										self.config_tester.run_common_tests()


						def 	__lowercase			(				self					:      Union[str, Any]       ):
										lowerCAmelCase						=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__snake_case       )


						def 	__lowercase			(				self					:      List[str]       ):
										lowerCAmelCase						=					self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
														lowerCAmelCase						=					type
														self.model_tester.create_and_check_model(*__snake_case       )


						def 	__lowercase			(				self					:      Optional[int]       ):
										lowerCAmelCase						=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*__snake_case       )


						def 	__lowercase			(				self					:      Tuple       ):
										lowerCAmelCase						=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*__snake_case       )


						@slow
						def 	__lowercase			(				self					:      Union[str, Any]       ):
										for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase						=					EsmModel.from_pretrained(__snake_case       )
														self.assertIsNotNone(__snake_case       )


						def 	__lowercase			(				self					:      Optional[Any]       ):
										lowerCAmelCase						=					self.model_tester.prepare_config_and_inputs()[0]
										lowerCAmelCase						=					EsmEmbeddings(config=__snake_case       )

										lowerCAmelCase						=					torch.as_tensor([[12, 31, 13, model.padding_idx]]       )
										lowerCAmelCase						=					torch.as_tensor(
										    [
										        [
										            0 + model.padding_idx + 1,
										            1 + model.padding_idx + 1,
										            2 + model.padding_idx + 1,
										            model.padding_idx,
										        ]
										    ]       )
										lowerCAmelCase						=					create_position_ids_from_input_ids(__snake_case    ,    model.padding_idx       )
										self.assertEqual(position_ids.shape    ,    expected_positions.shape       )
										self.assertTrue(torch.all(torch.eq(__snake_case    ,    __snake_case       )       )       )


						def 	__lowercase			(				self					:      str       ):
										lowerCAmelCase						=					self.model_tester.prepare_config_and_inputs()[0]
										lowerCAmelCase						=					EsmEmbeddings(config=__snake_case       )

										lowerCAmelCase						=					torch.empty(2    ,    4    ,    30       )
										lowerCAmelCase						=					[
										    0 + embeddings.padding_idx + 1,
										    1 + embeddings.padding_idx + 1,
										    2 + embeddings.padding_idx + 1,
										    3 + embeddings.padding_idx + 1,
										]
										lowerCAmelCase						=					torch.as_tensor([expected_single_positions, expected_single_positions]       )
										lowerCAmelCase						=					embeddings.create_position_ids_from_inputs_embeds(__snake_case       )
										self.assertEqual(position_ids.shape    ,    expected_positions.shape       )
										self.assertTrue(torch.all(torch.eq(__snake_case    ,    __snake_case       )       )       )


						@unittest.skip("""Esm does not support embedding resizing"""       )
						def 	__lowercase			(				self					:      Tuple       ):
										pass


						@unittest.skip("""Esm does not support embedding resizing"""       )
						def 	__lowercase			(				self					:      Optional[int]       ):
										pass




						@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""       )
						def 	__lowercase			(				self					:      Dict       ):
										pass






@require_torch
class    SCREAMING_SNAKE_CASE__       (       lowerCamelCase__       ):


						@slow
						def 	__lowercase			(				self					:      Any       ):
										with torch.no_grad():
														lowerCAmelCase						=					EsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D"""       )
														model.eval()
														lowerCAmelCase						=					torch.tensor([[0, 1, 2, 3, 4, 5]]       )
														lowerCAmelCase						=					model(__snake_case       )[0]

														lowerCAmelCase						=					33

														lowerCAmelCase						=					torch.Size((1, 6, vocab_size)       )
														self.assertEqual(output.shape    ,    __snake_case       )

														lowerCAmelCase						=					torch.tensor(
														    [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]]       )
														self.assertTrue(torch.allclose(output[:, :3, :3]    ,    __snake_case    ,    atol=1e-4       )       )




						@slow
						def 	__lowercase			(				self					:      str       ):
										with torch.no_grad():
														lowerCAmelCase						=					EsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D"""       )
														model.eval()

														lowerCAmelCase						=					torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]       )
														lowerCAmelCase						=					model(__snake_case       )[0]
														# compare the actual values for a slice.
														lowerCAmelCase						=					torch.tensor(
														    [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]]       )
														self.assertTrue(torch.allclose(output[:, :3, :3]    ,    __snake_case    ,    atol=1e-4       )       )










'''simple docstring'''






import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowerCAmelCase:   Dict					=		'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase:   Union[str, Any]					=		direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowerCAmelCase:   Union[str, Any]					=		re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowerCAmelCase:   Optional[Any]					=		re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCAmelCase:   int					=		re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCAmelCase:   List[Any]					=		[
    ('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
    ('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
    ('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
    ('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
    ('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
    ('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
    ('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
    ('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
    ('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
    (
        'zero-shot-object-detection',
        'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
        'AutoModelForZeroShotObjectDetection',
    ),
    ('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
    ('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
    ('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
    ('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
    (
        'table-question-answering',
        'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForTableQuestionAnswering',
    ),
    ('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
    ('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
    (
        'next-sentence-prediction',
        'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
        'AutoModelForNextSentencePrediction',
    ),
    (
        'audio-frame-classification',
        'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForAudioFrameClassification',
    ),
    ('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
    (
        'document-question-answering',
        'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForDocumentQuestionAnswering',
    ),
    (
        'visual-question-answering',
        'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForVisualQuestionAnswering',
    ),
    ('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
    (
        'zero-shot-image-classification',
        'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForZeroShotImageClassification',
    ),
    ('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
    ('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
    ('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]
def  lowerCamelCase__			(		_A							):
  a		:   Optional[Any]	  =	re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'					,     _A							)
  return [m.group(0							) for m in matches]
def  lowerCamelCase__			(		):
  a		:   Union[str, Any]	  =	transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  a		:   Union[str, Any]	  =	{
      config.replace('Config'					,     ''							): model_type for model_type, config in config_maping_names.items()
  }

  # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
  a		:   Union[str, Any]	  =	collections.defaultdict(_A							)
  a		:   Optional[Any]	  =	collections.defaultdict(_A							)
  a		:   int	  =	collections.defaultdict(_A							)

  # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
  for attr_name in dir(_A							):
    a		:   Optional[Any]	  =	None
    if _re_tf_models.match(_A							) is not None:
      a		:   List[str]	  =	tf_models
      a		:   str	  =	_re_tf_models.match(_A							).groups()[0]
    elif _re_flax_models.match(_A							) is not None:
      a		:   Optional[int]	  =	flax_models
      a		:   List[Any]	  =	_re_flax_models.match(_A							).groups()[0]
    elif _re_pt_models.match(_A							) is not None:
      a		:   int	  =	pt_models
      a		:   List[str]	  =	_re_pt_models.match(_A							).groups()[0]

    if lookup_dict is not None:
      while len(_A							) > 0:
        if attr_name in model_prefix_to_model_type:
          a		:   Optional[Any]	  =	True
          break
        # Try again after removing the last word in the name
        a		:   List[str]	  =	''.join(camel_case_split(_A							)[:-1]							)

  a		:   Any	  =	set(list(pt_models.keys()							) + list(tf_models.keys()							) + list(flax_models.keys()							)							)
  a		:   int	  =	list(_A							)
  all_models.sort()

  a		:   Optional[Any]	  =	{'model_type': all_models}
  a		:   List[str]	  =	[pt_models[t] for t in all_models]
  a		:   str	  =	[tf_models[t] for t in all_models]
  a		:   int	  =	[flax_models[t] for t in all_models]

  # Now let's use the auto-mapping names to make sure
  a		:   Any	  =	{}
  for t in all_models:
    if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
      a		:   Optional[int]	  =	'AutoProcessor'
    elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
      a		:   Union[str, Any]	  =	'AutoTokenizer'
    elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
      a		:   Tuple	  =	'AutoFeatureExtractor'
    else:
      # Default to AutoTokenizer if a model has nothing, for backward compatibility.
      a		:   str	  =	'AutoTokenizer'

  a		:   str	  =	[processors[t] for t in all_models]

  return pd.DataFrame(_A							)
def  lowerCamelCase__			(		_A							):
  a		:   List[Any]	  =	[
      transformers_module.models.auto.modeling_auto,
      transformers_module.models.auto.modeling_tf_auto,
      transformers_module.models.auto.modeling_flax_auto,
  ]
  for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
    a		:   Optional[Any]	  =	[model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
    a		:   Optional[int]	  =	[auto_class, f"""TF_{auto_class}""", f"""Flax_{auto_class}"""]
    # Loop through all three frameworks
    for module, cls, mapping in zip(_A					,     _A					,     _A							):
      # The type of pipeline may not exist in this framework
      if not hasattr(_A					,     _A							):
        continue
      # First extract all model_names
      a		:   List[str]	  =	[]
      for name in getattr(_A					,     _A							).values():
        if isinstance(_A					,     _A							):
          model_names.append(_A							)
        else:
          model_names.extend(list(_A							)							)

            # Add pipeline tag and auto model class for those models
      table.update({model_name: (pipeline_tag, cls) for model_name in model_names}							)

  return table
def  lowerCamelCase__			(		_A					,     _A							):
  a		:   Optional[int]	  =	get_frameworks_table()
  a		:   List[str]	  =	Dataset.from_pandas(_A							)

  a		:   Optional[Any]	  =	hf_hub_download(
      'huggingface/transformers-metadata'					,     'pipeline_tags.json'					,     repo_type='dataset'					,     token=_A							)
  a		:   List[Any]	  =	Dataset.from_json(_A							)
  a		:   Optional[Any]	  =	{
      tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
      for i in range(len(_A							)							)
  }
  a		:   Dict	  =	update_pipeline_and_auto_class_table(_A							)

  # Sort the model classes to avoid some nondeterministic updates to create false update commits.
  a		:   str	  =	sorted(table.keys()							)
  a		:   List[Any]	  =	pd.DataFrame(
      {
          'model_class': model_classes,
          'pipeline_tag': [table[m][0] for m in model_classes],
          'auto_class': [table[m][1] for m in model_classes],
      }							)
  a		:   Optional[Any]	  =	Dataset.from_pandas(_A							)

  with tempfile.TemporaryDirectory() as tmp_dir:
    frameworks_dataset.to_json(os.path.join(_A					,     'frameworks.json'							)							)
    tags_dataset.to_json(os.path.join(_A					,     'pipeline_tags.json'							)							)

    if commit_sha is not None:
      a		:   int	  =	(
          f"""Update with commit {commit_sha}\n\nSee: """
          f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
      )
    else:
      a		:   Optional[int]	  =	'Update'

    upload_folder(
        repo_id='huggingface/transformers-metadata'					,     folder_path=_A					,     repo_type='dataset'					,     token=_A					,     commit_message=_A					,     )
def  lowerCamelCase__			(		):
  a		:   int	  =	{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
  a		:   List[Any]	  =	transformers_module.pipelines.SUPPORTED_TASKS
  a		:   str	  =	[]
  for key in pipeline_tasks:
    if key not in in_table:
      a		:   Dict	  =	pipeline_tasks[key]['pt']
      if isinstance(_A					,     (list, tuple)							):
        a		:   List[Any]	  =	model[0]
      a		:   Union[str, Any]	  =	model.__name__
      if model not in in_table.values():
        missing.append(_A							)

  if len(_A							) > 0:
    a		:   List[str]	  =	', '.join(_A							)
    raise ValueError(
        'The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '
        f"""`utils/update_metadata.py`: {msg}. Please add them!"""							)


if __name__ == "__main__":
     lowerCAmelCase:   Tuple					=		argparse.ArgumentParser()
     parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
     parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
     parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
     lowerCAmelCase:   Optional[Any]					=		parser.parse_args()

     if args.check_only:
          check_pipeline_tags()
     else:
          update_metadata(args.token, args.commit_sha)